Novel artificial neural network and linear regression based equation for estimating visceral adipose tissue volume

Clinical Nutrition, vol. 39, pp. 3182–3188

Abstract

BACKGROUND: There is a growing interest in fast and reliable assessment of abdominal visceral adipose tissue (VAT) volume for risk stratification of metabolic disorders. However, imaging based measurement of VAT is costly and limited by scanner availability. Therefore, we aimed to develop equations to estimate abdominal VAT volume from simple anthropometric parameters and to assess whether linear regression based equations differed in performance from artificial neural network (ANN) based equations. METHODS: MRI-measured abdominal VAT volumes and anthropometric parameters of 5772 subjects (White ethnicity, age 45-76 years, 52.7% females) were obtained from the UK Biobank. Subjects were divided into the derivation sample (n = 5195) and the validation sample (n = 577). Basic models (age, sex, height, weight) and expanded models (basic model + waist circumference and hip circumference) were constructed from the derivation sample by linear regression and ANN respectively. Performance of the linear regression and ANN based equations in the validation sample were compared and estimating accuracies were evaluated by receiver-operating characteristic curves (ROC). RESULTS: and lower error measurements than linear regression equations. However, no statistical difference was found between linear regression equations and their ANN based counterparts in ROC analysis. Both linear regression and ANN based expanded equations presented higher estimating accuracies (76.9%-90.1%) than the basic equations (74.5%-87.5%) in ROC analysis. CONCLUSIONS: We present equations based on linear regression and artificial neural networks to estimate abdominal VAT volume by simple anthropometric parameters for middle-aged and elderly White population. These equations can be used to estimate VAT volume in general practice as well as population-based studies.

Authors 3

1. Ling Lin corresponding Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Albinusdreef 2, Postzone: C2-S, 2333 ZA, Leiden, the Netherlands. Electronic address: l.lin@lumc.nl

   Department of Radiology, Leiden University Medical Center, Albinusdreef 2, Postzone: C2-S, 2333 ZA, Leiden, the Netherlands

2. Ilona A. Dekkers corresponding Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Albinusdreef 2, Postzone: C2-S, 2333 ZA, Leiden, the Netherlands. Electronic address: i.a.dekkers@lumc.nl

   Department of Radiology, Leiden University Medical Center, Albinusdreef 2, Postzone: C2-S, 2333 ZA, Leiden, the Netherlands

3. Hildo J. Lamb corresponding Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Albinusdreef 2, Postzone: C2-S, 2333 ZA, Leiden, the Netherlands. Electronic address: h.j.lamb@lumc.nl

   Department of Radiology, Leiden University Medical Center, Albinusdreef 2, Postzone: C2-S, 2333 ZA, Leiden, the Netherlands